Explore the transformative impact of AI across banking, insurance, investment, and discover how to harness its power for your financial services business.
The quicker and less hassle a process is, the more appealing it is to customers. And the financial services industry has been leveraging AI for efficiency for a while now, so what’s different today?
Generative AI. It transcends mere automation. It goes beyond just interpreting data and generates unique outputs, unveils hidden patterns, and even predicts future outcomes. This opens doors for previously unimaginable applications in fintech.
From credit scoring that goes beyond traditional metrics to robo-advisors offering personalized investment strategies, AI is using data like never before to make financial products and services sharper. In this blog, we explore the most prominent use cases of AI in fintech along with some real-world examples.
Let’s begin with a brief overview of how AI is transforming the fintech industry.
AI’s impact on the financial industry
AI is driving transformation across the financial services industry, enabling firms to unlock new efficiencies, enhance risk management capabilities, and deliver superior customer experiences.
- Operational efficiency
AI significantly reduces operational overheads by automating labor-intensive and repetitive tasks like data entry, document processing, and reconciliation, ultimately leading to cost savings. Banks are most likely to benefit from it. Generative AI is expected to add new value of $200-$340 billion annually (equivalent to 9 to 15 percent of operating profits) for the banking sector.
- Enhanced security
Leveraging AI for real-time fraud detection can prevent losses and boost compliance. American Express’ AI decision engine analyzes over $1 trillion in transactions annually, minimizing fraud.
- Improved decision-making
AI models can process vast amounts of data from diverse sources to make faster, more accurate decisions around lending, insurance underwriting, trading strategies, etc. A Deloitte survey found that 85% of its respondents who used AI-based solutions in the pre-investment phase agreed that AI helped them generate an alpha strategy.
- Personalized customer experience
AI enables tailoring products, advising, and outreach at an individual level based on predictive analytics on customer needs and behavior patterns. This improves engagement and loyalty. Robo-advisors like Betterment provide hyper-personalized investment advice at scale.
This transformative impact of AI in the financial industry is largely driven by a diverse set of AI technologies, which we discuss below.
AI technologies used in fintech today
While AI covers a wide range of technologies, here are some of the key ones transforming financial services.
Equipped with these powerful technologies, AI is being applied in numerous innovative ways within the financial sector. Let’s explore some specific use cases.
AI in fintech use cases and applications with examples
In this section, we examine the top applications of AI in financial services, with real-world examples of how it is transforming financial processes.
A. Banking, lending, and insurance: Automating operations with AI
1. Document verification and KYC automation
AI is being increasingly used to automate the KYC process, with solutions such as intelligent document processing, digital customer onboarding, and biometric authentication.
- Information collection: AI-powered optical character recognition (OCR)can extract information like ID numbers, names, addresses, and more from identity documents (passports, driver’s licenses, utility bills, etc.).
- Document verification automation: ML models cross-check the extracted data against sanctions lists, watchlists, and other required databases, flagging potential risks or inconsistent information.
- Enhanced screening: Predictive analysis tools can detect doctored or fraudulent documents by analyzing subtle patterns not caught by the human eye.
By completing KYC in minutes rather than days, AI technologies significantly reduce costs, minimize errors, and improve customer satisfaction.
For example, Scotiabank, one of Canada’s Big Five banks, uses Google AI solutions such as NLP, Voice, and Vision capabilities to automate document processes and customer onboarding– thus improving customer interactions.
2. Automated loan processing
AI can fully automate loan processing, eliminating administrative overhead and enabling faster disbursements.
- Credit scoring and loan underwriting: ML models, trained on amounts of historical loan data, analyze the credit score and other application details (debt-to-income ratio, types of credit used, etc.) to evaluate the risk of lending money to a borrower.
- Asset evaluation: For mortgage lending, AI can automate property appraisals by analyzing listing data and imagery and comparing them with peer valuations.
- Policy adherence: AI models also scrutinize documents to ensure that KYC and lending policies are met before approval.
- Fraud detection: By understanding patterns from past fraud cases, AI flags potentially dubious loan applications for review.
Lending startups like Upstart use AI at every step of the lending process. It allows applying for a fast personal loan, auto refinancing, or debt consolidation– all online.
Simform developed an online P2P (peer-to-peer) lending platform
It directly connects borrowers with individual investors, sidestepping traditional intermediaries like banks.
We used TrueLayer’s open banking API to integrate with various banks and enable secure transactions. Additionally, it supports SCA as required by PSD2 regulations. We employed microservices to efficiently manage critical modules such as loan calculations, affiliation processes, and user verification. VPN ensured secure communication between these modules, making it a highly responsive and reliable system.
We also built robust compliance frameworks, including the Financial Conduct Authority (FCA), to handle the complex regulatory landscape, ensuring timely updates and adjustments in response to new FCA directives.
3. Underwriting and claims management
In insurance, underwriters evaluate risk factors associated with prospective clients and coverage types to determine policy terms and pricing. This complex process involves analyzing large datasets on customer profiles, health records, etc.
Intelligent underwriting
ML models trained on these historical data can accurately quantify risks and automate underwriting decisions, at least for low-risk cases.
Precision pricing
For higher customer satisfaction and retention, you can offer tailored pricing instead of broad demographic-based pricing. AI aids this with:
- Granular risk modeling: Processing large multi-dimensional data sets including behavioral data, market indicators, etc., to produce highly granular and precise risk quantification.
- Dynamic pricing: Using AI recommendation engines that adjust pricing elements (e.g., premiums, interest rates, fees) in real-time based on the specific risk profiles.
- Forecasting impact: Forecasting impact of pricing strategy on critical metrics like market share, customer churn, and profit margins to optimize the pricing structure.
This approach is similar to some of the “individualized data” use cases of AI in insurance. For instance, Progressive, a leading insurance company in the USA, collects data about individual drivers to predict their risk of accidents better.
Another example is CAPE Analytics, a computer vision startup that turns geospatial data into actionable insights to optimize the underwriting process for home insurers.
Simform developed a telematics-based solution for Scandinivia’s largest insurer, Tryg.
It uses ML for real-time predictive analytics based on data collected from fleet sensors. It helps find emerging vehicle health issues for downstream processing, such as insurance claims.
